Statistical Behavior Guided Block Allocation in Hybrid Cache-Based Edge Computing for Cyber-Physical-Social Systems
Fanfan Shen, Chao Xu, Jun Zhang
Abstract
In Cyber-Physical-Social Systems (CPSS), large-scale data are continually generated from edge computing devices in our daily lives. These heterogeneous data collected from CPSS are urgently needed to be processed efficiently with low power consumption. Hybrid cache based edge computing can accelerate the computing speed for the edge devices. Hybrid cache consisting of spin-transfer torque RAM (STT-RAM) and static RAM (SRAM) has been proposed as last level cache (LLC) for energy efficiency recently in CPSS. However, the write operations on STT-RAM suffer from considerably higher energy consumption as well as longer latency than SRAM, the proper allocation of data blocks has a significant effect on both energy consumption and performance in the hybrid cache. So it is very useful to adjust the data allocation for the asymmetric-access in hybrid cache. To enhance the performance of hybrid cache, this paper proposes a novel statistical behavior guided block allocation (SBOA) scheme to process CPSS data. The key idea is to estimate the cache block characteristics based on the statistical behavior of data read/write re-references. We design a theoretical analysis model to optimize the energy consumption and guide block allocation in both SRAM region and STT-RAM region. Experimental results demonstrate that the proposed scheme reduces the dynamic energy consumption by 18.5%, and reduces execution time by 7.4% on average compared to the baseline with negligible overhead.